2024/12/13 by Plácido A. Souza Neto, Neto, Plácido A Souza
Computer Science · #Graph Theory and Algorithms #Advanced Graph Neural Networks #Semantic Web and Ontologies
paper · pdf · doi:10.48550/arxiv.2412.09940
Graphs face challenges when dealing with massive datasets. They are essential\ntools for modeling interconnected data and often become computationally\nexpensive. Graph embedding techniques, on the other hand, provide an efficient\napproach. By projecting complex graphs into a lower-dimensional space, these\ntechniques simplify the analysis and processing of large-scale graphs. By\ntransforming graphs into vectors, it simplifies the analysis and processing of\nlarge-scale datasets. Several approaches, such as GraphSAGE, Node2Vec, and\nFastRP, offer efficient methods for generating graph embeddings. By storing\nembeddings as node properties, it is possible to compare different embedding\ntechniques and evaluate their effectiveness for specific tasks. This\nflexibilityallows for dynamic updates to embeddings and facilitates\nexperimentation with different approaches. By analyzing these embeddings, one\ncan extract valuable insights into the relationships between nodes and their\nsimilarities within the embedding space\n